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Paper Citation Record · LEDGER

Estimating Learnability in the Sublinear Data Regime

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1805.01626.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1805.01626 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T10:49:01.943175Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-08T14:35:34.157534Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e9f718bc-a829-4977-b81a-c0c226a123d3 · inbound

Feature Gradients: Scalable Feature Selection via Discrete Relaxation cites this paper.

Feature Gradients: Scalable Feature Selection via Discrete Relaxation Estimating Learnability in the Sublinear Data Regime

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-14T10:49:01.943175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:49:01.943175Z digest=sha256:a7eb9df4d2c633a4145461661e043c803b03f83e9e7baecfd022583249eb4aea

Observation d2151f80-4b11-4b25-9e10-7738930a6936 · inbound

Are all models wrong? Fundamental limits in distribution-free empirical model falsification cites this paper.

Are all models wrong? Fundamental limits in distribution-free empirical model falsification Estimating Learnability in the Sublinear Data Regime

Reference 26

Resolution
verified exact
local_arxiv, observed 2026-08-08T14:35:34.163594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-08T14:35:33.823528Z digest=sha256:538158eb06a8ffe7a043f3d6adee3d5c06f3f63b510a9e587c8202974747a844